RecMax : Exploiting Recommender Systems for Fun and Profit

RecMax: Exploiting Recommender Systems for Fun and Profit AmitGoyal Laks V. S. Lakshmanan University of British Columbia http://cs.ubc.ca/~goyal

Recommender Systems Products Movies Music Videos News Websites

RecMax – Recommendation Maximization • Previous research mostly focused on improving accuracy of recommendations. • In this paper, we propose a novel problem RecMax (short for Recommendation Maximization). • Can we launch a targeted marketing campaign over an existing operational Recommender System?

Consider an item in a Recommender System Flow of information • Because of these ratings, the item may be recommended to some other users. • Some users rate the item • (seed users) RecMax: Can we strategically select the seed users?

RecMax 5 Flow of information • Users to whom the item is recommended • Seed Users Select kseed users such that ifthey provide high ratings to a new product, thenthe number of other users to whom the product is recommended (hit score) by the underlying recommender system algorithm is maximum.

RecMax – Problem Formulation Recommendation List for user v rating threshold of user v (denoted by θv) Number of recommendations are l For a new item i, if expected rating R(v,i) >θv, then the new item is recommended to v The goal of RecMax is to find a seed set S such that hit score f(S) is maximized.

Benefits of RecMax • Targeted marketing in Recommender Systems • Marketers can effectively advertise new products on a Recommender System platform. • Business opportunity to Recommender System platform. • Similar to Influence Maximization problem in spirit. • Beneficial to seed users • They get free/discounted samples of a new product. • Helpful to other users • They receive recommendations of new products – solution to cold start problem.

A key Challenge – Wide diversity of Recommender Systems Similarity functions: Cosine, Pearson, Adjusted Cosine etc Due to this wide diversity, it is very difficult to study RecMax

Outline • What is RecMax? • Does Seeding Help? – Preliminary Experiments. • Theoretical Analysis of RecMax. • Experiments. • Conclusions and Future Work.

Does Seeding Help? • Dataset: Movielens • Recommender System: User-based • Seeds are picked randomly. • Recall that Hit Score is the number of users to whom the product is recommended. Abudget of 500 can get a hit score of 5091 (10x) (User-based)

Does Seeding Help? • Dataset: Movielens • Recommender System: Item-based • Seeds are picked randomly. • Recall that Hit Score is the number of users to whom the product is recommended. Abudget of 20 can get a hit score of 636 (30x) (Item-based)

Key Theoretical Results • RecMax is NP-hard to solve exactly. • RecMax is NP-hard to approximate within a factor to 1/|V|(1-ε) for any ε> 0. • No reasonable approximation algorithm can be developed. • RecMax is as hard as Maximum Independent Set Problem. • Under both User-based and under Item-based.

Why is RecMax so hard? (1/2) • We introduce a helper problem – Maximum Encirclement Problem • find a set S of size k such that it encircles maximum number of nodes in the graph. A B E C D • Nodes {B,C} encircle node A. • Nodes {B,C,E} encircle node D. • Thus, {B,C,E} encircle A and D.

Why is RecMax so hard? (2/2) • Set {B,C,E} is a solution to Maximum Encirclement Problem (for k=3). • Nodes {A,D} form Maximum Independent Set. A B E • Reduction: Nodes {B,C,E} must rate the new item highly for the item to be recommended to A and D. • RecMax is as hard as Maximum Independent Set, and hence NP-hard to approximate within a factor to 1/|V|(1-ε) C D

Discussion (1/2) • We show hardness for User-based and Item-based methods. • What about Matrix Factorization? • Most likely hardness would remain (future work).

Discussion (2/2) • Since the problem is hard to approximate, does it make sense to study? • YES, as we saw earlier, even a random heuristic fetches impressive gains. • We explore several natural heuristics and compare them. • What about sophisticated heuristics (future work).

Datasets

Heuristics • Random: Seed set is selected randomly. The process is repeated several times and average is taken. • Most-Active: Top-k users with most number of ratings. • Most-Positive: Top-k users with most positive average ratings. • Most-Critical: Top-k users with most critical average ratings.

Heuristics • Most-Central: Top-k central users.

User-Based Recommender Systems

Comparison – Hit Score achieved • Dataset: Movielens • Recommender System: User-based Most Central, Most Positive and Random perform good here.

Comparison – Hit Score achieved • Dataset: Yahoo! Music • Recommender System: User-based Most Positive, Most Central perform good here.

Comparison – Hit Score achieved • Dataset: Jester Joke • Recommender System: User-based Most Central out-performs all other heuristics.

Key Takeaways • Even the simple heuristics perform well. • With a budget of 300, Most-Central heuristic achieves hit score of 4.4K, 3.4K and 15.6K on Movielens, Yahoo! and Jester respectively. • Depending on the data set, we may encounter a “tipping point” – a minimum seeding is needed for the results to be impressive.

Item-Based Recommender Systems

Comparison – Hit Score achieved • Dataset: Movielens • Recommender System: Item-based Most Central performs good here.

Comparison – Hit Score achieved • Dataset: Yahoo! Music • Recommender System: Item-based Most Central performs good here.

Comparison – Hit Score achieved • Dataset: Jester Joke • Recommender System: Item-based Most Central, Random and Most-Active performs good here.

Key Takeaways • Again, the simple heuristics perform well. • Hit score achieved in Item-based is much lower than in User-based. • Thus, much less seeding is required to achieve maximum possible hit score. • Overall, Most-Central performs well. • The difference of Most-Central with baseline Random is not much. • We need better heuristics (future work).

User-Based vs Item-Based

User-based vs Item-based • Dataset: Yahoo! Music • Initial rise of hit score is steeper in Item-based. • Hit score saturates much earlier in Item-based. Eventual hit score that can be achieved is much more in User-based.

User-based vs Item-based Seed Sets are different in both methods.

Our Contributions • The main goal of the paper is to propose and study a novel problem that we call RecMax • Select k seed users such that if they endorse a new product by providing relatively high ratings, the number of users to whom the product is recommended (hit score) is maximum. • We focus on User-based and Item-based recommender systems. • We offer empirical evidence that seeding does help in boosting the number of recommendations

Our Contributions • We perform a thorough theoretical analysis of RecMax. • RecMax is NP-hard to solve exactly. • RecMax is NP-hard to approximate within any reasonable factor. • Given this hardness, we explore several natural heuristics on 3 real world datasets and report our findings. • Even simple heuristics like Most-Central provide impressive gains • This makes RecMax an interesting problem for targeted marketing in recommender systems.

Future Work • RecMax is a new problem and has real applications – our work is just the first work. • Developing better heuristics. • Studying RecMax on more sophisticated recommender systems algorithms • Matrix Factorization.

Thanks and Questions RecMax: Exploiting Recommender Systems for Fun and Profit Amit Goyal LaksV. S. Lakshmanan University of British Columbia University of British Columbia http://cs.ubc.ca/~goyal

Heuristics • Most-Central: Top-k central users.

RecMax : Exploiting Recommender Systems for Fun and Profit